Microglia Mediate the Occurrence and Development of Alzheimer's Disease Through Ligand-Receptor Axis Communication

Single-Nucleus Landscape of Glial Cells and Neurons in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease with a projected significant increase in incidence. Therefore, this study analyzed single-nucleus AD data to provide a theoretical basis for the clinical development and treatment of AD. We downloaded AD-related monocyte data from the Gene Expression Omnibus database, annotated cells, compared cell abundance between groups, and investigated glial and neuronal cell biological processes and pathways through functional enrichment analysis. Furthermore, we constructed a global regulatory network for AD based on cell communication and ecological analyses. Our findings revealed increased abundance of Capping Protein Regulator And Myosin 1 linker 1 (CARMIL1)+ astrocytes (AST), Immunoglobulin Superfamily Member 21 (IGSF21)+ microglia (MIC), SRY-Box Transcription Factor 6 (SOX6)+ inhibitory neurons (InNeu), and laminin alpha-2 chain (LAMA2)+ oligodendrocytes (OLI) cell subgroups in tissues of patients with AD, while prostaglandin D2 synthase (PTGDS)+ AST, Src Family Tyrosine Kinase (FYN)+ MIC, and Proteolipid Protein 1 (PLP1)+ InNeu subgroups specifically decreased. We found that the cell phenotype of patients with AD shifted from a simpler to a more complex state compared to the control group. Cell communication analysis revealed strong communication between MIC and NEU. Furthermore, AST, MIC, NEU, and OLI were involved in oxidative stress- and inflammation-related pathways, potentially contributing to disease development. This study provides a theoretical basis for further exploring the specific mechanisms underlying AD.

Integrative bioinformatic approach reveals novel melatonin-related biomarkers for Alzheimer's disease

BACKGROUND

Melatonin (MLT) can improve mitophagy, thereby ameliorating cognitive deficits in Alzheimer's disease (AD) patients. Hence, our research focused on the potential value of MLT-related genes (MRGs) in AD through bioinformatic analysis.

METHODS

First, the key cells in the single-cell dataset GSE138852 were screened out based on the proportion of annotated cells and Fisher's test between the AD and control groups. The differentially expressed genes (DEGs) in the key cell and GSE5281 datasets were identified, and the MRGs in GSE5281 were selected via weighted gene coexpression network analysis. After intersecting two sets of DEGs and MRGs, we performed Mendelian randomization analysis to identify the MRGs causally related to AD. Biomarkers were further ascertained through receiver operating characteristic curve (ROC) and expression analysis in GSE5281 and GSE48350. Furthermore, gene set enrichment analysis, immune infiltration analysis and correlation analysis with metabolic pathways were conducted, as well as construction of a regulator network and molecular docking.

RESULTS

According to the Fisher test, oligodendrocytes were regarded as key cells due to their excellent abundance in the GSE138852 dataset, in which there were 281 DEGs between the AD and control groups. After overlapping with 3,490 DEGs and 550 MRGs in GSE5281, four genes were found to be causally related to AD, namely, G protein-coupled receptor, family C, group 5, member B (GPRC5B), Methyltransferase-like protein 7 A (METTL7A), NF-κB inhibitor alpha (NFKBIA) and RAS association domain family 4(RASSF4). Moreover, GPRC5B, NFKBIA and RASSF4 were deemed biomarkers, except for METTL7A, because of their indistinctive expression between the AD and control groups. Biomarkers might be involved in oxidative phosphorylation, adipogenesis and heme metabolism. Moreover, T helper type 17 cells, natural killer cells and CD56dim natural killer cells were significantly correlated with biomarkers. Transcription factors (GATA2, POU2F2, NFKB1, etc.) can regulate the expression of biomarkers. Finally, we discovered that all biomarkers could bind to MLT with a strong binding energy.

CONCLUSION

Our study identified three novel biomarkers related to MLT for AD, namely, GPRC5B, NFKBIA and RASSF4, providing a novel approach for the investigation and treatment of AD patients.

Identification of altered immune cell types and molecular mechanisms in Alzheimer's disease progression by single-cell RNA sequencing

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by gradual loss of cognitive function. Understanding the molecular mechanisms is crucial for developing effective therapies.

Methods

Data from single-cell RNA sequencing (scRNA-seq) in the GSE181279 dataset and gene chips in the GSE63060 and GSE63061 datasets were collected and analyzed to identify immune cell types and differentially expressed genes. Cell communication, pseudotime trajectory, enrichment analysis, co- expression network, and short time-series expression miner were analyzed to identify disease-specific molecular and cellular mechanisms.

Results

We identified eight cell types (B cells, monocytes, natural killer cells, gamma-delta T cells, CD8+ T cells, Tem/Temra cytotoxic T cells, Tem/Trm cytotoxic T cells, and mucosal-associated invariant T cells) using scRNA-seq. AD samples were enriched in monocytes, CD8+ T cells, Tem/Temra cytotoxic T cells, and Tem/Trm cytotoxic T cells, whereas samples from healthy controls were enriched in natural killer and mucosal-associated invariant T cells. Five co-expression modules that were identified through weighted gene correlation network analysis were enriched in immune- inflammatory pathways. Candidate genes with higher area under the receiver operating characteristic curve values were screened, and the expression trend of Ubiquitin-Fold Modifier Conjugating Enzyme 1 (UFC1) gradually decreased from healthy controls to mild cognitive impairment and then to AD.

Conclusion

Our study suggests that peripheral immune cells may be a potential therapeutic target for AD. Candidate genes, particularly UFC1, may serve as potential biomarkers for progression of AD.

Advancements in Single-Cell RNA Sequencing Research for Neurological Diseases

Neurological diseases are a major cause of the global burden of disease. Although the mechanisms of the occurrence and development of neurological diseases are not fully clear, most of them are associated with cells mediating neuroinflammation. Yet medications and other therapeutic options to improve treatment are still very limited. Single-cell RNA sequencing (scRNA-seq), as a delightfully potent breakthrough technology, not only identifies various cell types and response states but also uncovers cell-specific gene expression changes, gene regulatory networks, intercellular communication, and cellular movement trajectories, among others, in different cell types. In this review, we describe the technology of scRNA-seq in detail and discuss and summarize the application of scRNA-seq in exploring neurological diseases, elaborating the corresponding specific mechanisms of the diseases as well as providing a reliable basis for new therapeutic approaches. Finally, we affirm that scRNA-seq promotes the development of the neuroscience field and enables us to have a deeper cellular understanding of neurological diseases in the future, which provides strong support for the treatment of neurological diseases and the improvement of patients' prognosis.

Bibliometric and visual analysis of single-cell multiomics in neurodegenerative disease arrest studies

Background

Neurodegenerative diseases are progressive disorders that severely diminish the quality of life of patients. However, research on neurodegenerative diseases needs to be refined and deepened. Single-cell polyomics is a technique for obtaining transcriptomic, proteomic, and other information from a single cell. In recent years, the heat of single-cell multiomics as an emerging research tool for brain science has gradually increased. Therefore, the aim of this study was to analyze the current status and trends of studies related to the application of single-cell multiomics in neurodegenerative diseases through bibliometrics.

Result

A total of 596 publications were included in the bibliometric analysis. Between 2015 and 2022, the number of publications increased annually, with the total number of citations increasing significantly, exhibiting the fastest rate of growth between 2019 and 2022. The country/region collaboration map shows that the United States has the most publications and cumulative citations, and that China and the United States have the most collaborations. The institutions that produced the greatest number of articles were Harvard Medical School, Skupin, Alexander, and Wiendl. Among the authors, Heinz had the highest output. Mathys, H accumulated the most citations and was the authoritative author in the field. The journal Nature Communications has published the most literature in this field. A keyword analysis reveals that neurodegenerative diseases and lesions (e.g., Alzheimer's disease, amyloid beta) are the core and foundation of the field. Conversely, single-cell multiomics related research (e.g., single-cell RNA sequencing, bioinformatics) and brain nerve cells (e.g., microglia, astrocytes, neural stem cells) are the hot frontiers of this specialty. Among the references, the article "Single-cell transcriptomic analysis of Alzheimer's disease" is the most frequently cited (1,146 citations), and the article "Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq" was the most cited article in the field.

Conclusion

The objective of this study is to employ bibliometric methods to visualize studies related to single-cell multiomics in neurodegenerative diseases. This will enable us to summarize the current state of research and to reveal key trends and emerging hotspots in the field.

Downregulation of miR-181c-5p in Alzheimer's disease weakens the response of microglia to Aβ phagocytosis

Alzheimer's disease (AD) is an age-associated neurodegenerative disease. Recently, studies have demonstrated the potential involvement of microRNA-181c-5p (miR-181c-5p) in AD. However, the mechanism through which miR-181c-5p is responsible for the onset and progression of this disease remains unclear, and our study aimed to explore this problem. Differential expression analysis of the AD dataset was performed to identify dysregulated genes. Based on hypergeometric analysis, AD differential the upstream regulation genes miR-181c-5p was found. We constructed a model where SH-SY5Y and BV2 cells were exposed to Aβ1-42 to simulate AD. Levels of tumor necrosis factor-alpha, interleukin-6, and IL-1β were determined using enzyme-linked immunosorbent assay or reverse transcription quantitative polymerase chain reaction. Phosphorylation levels of p-P38 and P38 were detected by Western blot. The level of apoptosis in BV2 cells under Aβ1-42 stress was exacerbated by miR-181c-5p mimic. Downregulated miR-181c-5p impaired the phagocytosis and degradation of Aβ by BV2 cells. The release of proinflammatory cytokines in BV2 cells with Aβ1-42 stress was alleviated by miR-181c-5p upregulation. Additionally, miR-181c-5p downregulation alleviated the phosphorylation of P38 in Aβ1-42-induced SH-SY5Y cells. In conclusion, miR-181c-5p improves the phagocytosis of Aβ by microglial cells in AD patients, thereby reducing neuroinflammation.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: an integrative study of single-nucleus transcriptomes and genetic association

BACKGROUND

Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

METHODS

We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database.

RESULTS

We identified 190 dysregulated LR interactions across six major cell types in AD PFC, of which 107 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in the astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 44 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport,' among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs.

CONCLUSIONS

Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

Oligodendrocyte progenitor cells in Alzheimer's disease: from physiology to pathology

Oligodendrocyte progenitor cells (OPCs) play pivotal roles in myelin formation and phagocytosis, communicating with neighboring cells and contributing to the integrity of the blood-brain barrier (BBB). However, under the pathological circumstances of Alzheimer's disease (AD), the brain's microenvironment undergoes detrimental changes that significantly impact OPCs and their functions. Starting with OPC functions, we delve into the transformation of OPCs to myelin-producing oligodendrocytes, the intricate signaling interactions with other cells in the central nervous system (CNS), and the fascinating process of phagocytosis, which influences the function of OPCs and affects CNS homeostasis. Moreover, we discuss the essential role of OPCs in BBB formation and highlight the critical contribution of OPCs in forming CNS-protective barriers. In the context of AD, the deterioration of the local microenvironment in the brain is discussed, mainly focusing on neuroinflammation, oxidative stress, and the accumulation of toxic proteins. The detrimental changes disturb the delicate balance in the brain, impacting the regenerative capacity of OPCs and compromising myelin integrity. Under pathological conditions, OPCs experience significant alterations in migration and proliferation, leading to impaired differentiation and a reduced ability to produce mature oligodendrocytes. Moreover, myelin degeneration and formation become increasingly active in AD, contributing to progressive neurodegeneration. Finally, we summarize the current therapeutic approaches targeting OPCs in AD. Strategies to revitalize OPC senescence, modulate signaling pathways to enhance OPC differentiation, and explore other potential therapeutic avenues are promising in alleviating the impact of AD on OPCs and CNS function. In conclusion, this review highlights the indispensable role of OPCs in CNS function and their involvement in the pathogenesis of AD. The intricate interplay between OPCs and the AD brain microenvironment underscores the complexity of neurodegenerative diseases. Insights from studying OPCs under pathological conditions provide a foundation for innovative therapeutic strategies targeting OPCs and fostering neurodegeneration. Future research will advance our understanding and management of neurodegenerative diseases, ultimately offering hope for effective treatments and improved quality of life for those affected by AD and related disorders.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: An integrative study of single-nucleus transcriptomes and genetic association

Background

Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

Methods

We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database.

Results

We identified 316 dysregulated LR interactions across six major cell types in AD PFC, of which 210 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 60 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport', among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs.

Conclusions

Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models

Background

Despite tremendous progress in diagnosis and prediction of Alzheimer's disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.

Methods

By differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.

Results

Screened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.

Conclusion

The LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

The molecular basis of the dichotomous functionality of MAP4K4 in proliferation and cell motility control in cancer

The finely tuned integration of intra- and extracellular cues by components of the mitogen-activated protein kinase (MAPK) signaling pathways controls the mutually exclusive phenotypic manifestations of uncontrolled growth and tumor cell dissemination. The Ser/Thr kinase MAP4K4 is an upstream integrator of extracellular cues involved in both proliferation and cell motility control. Initially identified as an activator of the c-Jun N-terminal kinase (JNK), the discovery of diverse functions and additional effectors of MAP4K4 beyond JNK signaling has considerably broadened our understanding of this complex kinase. The implication of MAP4K4 in the regulation of cytoskeleton dynamics and cell motility provided essential insights into its role as a pro-metastatic kinase in cancer. However, the more recently revealed role of MAP4K4 as an activator of the Hippo tumor suppressor pathway has complicated the understanding of MAP4K4 as an oncogenic driver kinase. To develop a better understanding of the diverse functions of MAP4K4 and their potential significance in oncogenesis and tumor progression, we have collected and assessed the current evidence of MAP4K4 implication in molecular mechanisms that control proliferation and promote cell motility. A better understanding of these mechanisms is particularly relevant in the brain, where MAP4K4 is highly expressed and under pathological conditions either drives neuronal cell death in neurodegenerative diseases or cell dissemination in malignant tumors. We review established effectors and present novel interactors of MAP4K4, which offer mechanistic insights into MAP4K4 function and may inspire novel intervention strategies. We discuss possible implications of novel interactors in tumor growth and dissemination and evaluate potential therapeutic strategies to selectively repress pro-oncogenic functions of MAP4K4.

Potential biomarkers of Alzheimer’s disease and cerebral small vessel disease

Background

Cerebral small vessel disease (CSVD) is associated with the pathogenesis of Alzheimer’s disease (AD). Effective treatments to alleviate AD are still not currently available. Hence, we explored markers and underlying molecular mechanisms associated with AD by utilizing gene expression profiles of AD and CSVD patients from public databases, providing more options for early diagnosis and its treatment.

Methods

Gene expression profiles were collected from GSE63060 (for AD) and GSE162790 (for CSVD). Differential analysis was performed between AD and mild cognitive impairment (MCI) or CSVD progression and CSVD no-progression. In both datasets, differentially expressed genes (DEGs) with the same expression direction were identified as common DEGs. Then protein-protein interaction (PPI) network was constructed for common DEGs. Differential immune cells and checkpoints were calculated between AD and MCI.

Results

A total of 146 common DEGs were identified. Common DEGs were mainly enriched in endocytosis and oxytocin signaling pathways. Interestingly, endocytosis and metabolic pathways were shown both from MCI to AD and from CSVD no-progression to CSVD progression. Moreover, SIRT1 was identified as a key gene by ranking degree of connectivity in the PPI network. SIRT1 was associated with obesity-related genes and metabolic disorders. Additionally, SIRT1 showed correlations with CD8 T cells, NK CD56 bright cells, and checkpoints in AD.

Conclusion

The study revealed that the progression of AD is associated with abnormalities in gene expression and metabolism and that the SIRT1 gene may serve as a promising therapeutic target for the treatment of AD.

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

Neurodegenerative diseases such as Alzheimer’s disease (AD) are an increasing public health challenge. There is an urgent need to shift the focus to accurate detection of clinical AD at the physical examination stage. The purpose of this study was to identify biomarkers for AD diagnosis. Differential expression analysis was performed on a dataset including prefrontal cortical samples and peripheral blood samples of AD to identify shared differentially expressed genes (DEGs) shared between the two datasets. In addition, a minimum absolute contraction and selection operator (LASSO) model based on shared-DEGs identified nine signature genes (MT1X, IGF1, DLEU7, TRIM36, PTPRC, WNK2, SPG20, C8orf59, and BRWD1) that accurately predict AD occurrence. Enrichment analysis showed that the signature gene was significantly associated with the AD-related p53 signaling pathway, T-cell receptor signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. Thus, our results identify not only biomarkers for diagnosing AD but also potentially specific pathways. The AD biomarkers proposed in this study could serve as indicators for prevention and diagnosis during physical examination.

The Association of Serum Uric Acid Level, Gout, and Alzheimer's Disease: A Bidirectional Mendelian Randomization Study

BACKGROUND

The relationship between serum uric acid (UA) and Alzheimer's disease (AD) risk still remained ambiguous despite extensive attempts.

OBJECTIVE

Via the two-sample Mendelian randomization (MR) design, we aimed to examine the bidirectional causal relationships of serum UA, gout, and the risk of AD.

METHODS

Genetic variants of UA, gout, and AD were extracted from published genome-wide association summary statistics. The inverse-variance weighted (IVW, the primary method), and several sensitivity methods (MR-Egger, weighted median, and weighted mode) were used to calculate the effect estimates. Egger regression, MR-PRESSO and leave-one-SNP-out analysis were performed to identify potential violations.

RESULTS

Genetic proxies for serum UA concentration [odds ratio (ORIVW) = 1.09, 95% confidence interval (CI) = 1.01-1.19, p = 0.031] were related with an increased risk of AD using 25 single nucleotide polymorphisms (SNPs). This causal effect was confirmed by sensitivity analyses including MR-Egger (1.22, 1.06-1.42, p = 0.014), weighted median (1.18, 1.05-1.33, p = 0.006), and weighted mode (1.20, 1.07-1.35, p = 0.005) methods. No evidence of notable directional pleiotropy and heterogeneity were identified (p >  0.05). Three SNPs (rs2078267, rs2231142, and rs11722228) significantly drove the observed causal effects. Supportive causal effect of genetically determined gout on AD risk was demonstrated using two SNPs (ORIVW = 1.05, 95% CI = 1.00-1.11, p = 0.057). No reverse causal effects of AD on serum UA levels and gout risk were found.

CONCLUSION

The findings revealed a causal relationship between elevated serum UA level and AD risk. However, further research is still warranted to investigate whether serum UA could be a reliable biomarker and therapeutic target for AD.

REPS1 as a Potential Biomarker in Alzheimer’s Disease and Vascular Dementia

Vascular dementia (VD) and Alzheimer’s disease (AD) are common types of dementia for which no curative therapies are known. In this study, we identified hub genes associated with AD and VD in order to explore new potential therapeutic targets. Genes differentially expressed in VD and AD in all three datasets (GSE122063, GSE132903, and GSE5281) were identified and used to construct a protein–protein interaction network. We identified 10 modules containing 427 module genes in AD and VD. Module genes showing an area under the diagnostic curve > 0.60 for AD or VD were used to construct a least absolute shrinkage and selection operator model and were entered into a support vector machine-recursive feature elimination algorithm, which identified REPS1 as a hub gene in AD and VD. Furthermore, REPS1 was associated with activation of pyruvate metabolism and inhibition of Ras signaling pathway. Module genes, together with differentially expressed microRNAs from the dataset GSE46579, were used to construct a regulatory network. REPS1 was predicted to bind to the microRNA hsa_miR_5701. Single-sample gene set enrichment analysis was used to explore immune cell infiltration, which suggested a negative correlation between REPS1 expression and infiltration by plasmacytoid dendritic cells in AD and VD. In conclusion, our results suggest core pathways involved in both AD and VD, and they identify REPS1 as a potential biomarker of both diseases. This protein may aid in early diagnosis, monitoring of treatment response, and even efforts to prevent these debilitating disorders.

Identification and Validation of Aging-Related Genes in Alzheimer’s Disease

Aging is recognized as the key risk factor for Alzheimer’s disease (AD). This study aimed to identify and verify potential aging-related genes associated with AD using bioinformatics analysis. Aging-related differential expression genes (ARDEGs) were determined by the intersection of limma test, weighted correlation network analysis (WGCNA), and 1153 aging and senescence-associated genes. Potential biological functions and pathways of ARDEGs were determined by GO, KEGG, GSEA, and GSVA. Then, LASSO algorithm was used to identify the hub genes and the diagnostic ability of the five ARDEGs in discriminating AD from the healthy control samples. Further, the correlation between hub ARDEGs and clinical characteristics was explored. Finally, the expression level of the five ARDEGs was validated using other four GEO datasets and blood samples of patients with AD and healthy individuals. Five ARDEGs (GFAP, PDGFRB, PLOD1, MAP4K4, and NFKBIA) were obtained. For biological function analysis, aging, cellular senescence, and Ras protein signal transduction regulation were enriched. Diagnostic ability of the five ARDEGs in discriminating AD from the control samples demonstrated a favorable diagnostic value. Eventually, quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) validation test revealed that compared with healthy controls, the mRNA expression level of PDGFRB, PLOD1, MAP4K4, and NFKBIA were elevated in AD patients. In conclusion, this study identified four ARDEGs (PDGFRB, PLOD1, MAP4K4, and NFKBIA) associated with AD. They provide an insight into potential novel biomarkers for diagnosing AD and monitoring progression.

Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing

The incidence of Alzheimer’s disease (AD) is constantly increasing as the older population grows, and no effective treatment is currently available. In this study, we focused on the identification of AD molecular subtypes to facilitate the development of effective drugs. AD sequencing data collected from the Gene Expression Omnibus (GEO) database were subjected to cluster sample analysis. Each sample module was then identified as a specific AD molecular subtype, and the biological processes and pathways were verified. The main long non-coding RNAs and transcription factors regulating each “typing pathway” and their potential mechanisms were determined using the RNAInter and TRRUST databases. Based on the marker genes of each “typing module,” a classifier was developed for molecular typing of AD. According to the pathways involved, five sample clustering modules were identified (mitogen-activated protein kinase, synaptic, autophagy, forkhead box class O, and cell senescence), which may be regulated through multiple pathways. The classifier showed good classification performance, which may be useful for developing novel AD drugs and predicting their indications.

Neuroinflammation and Oxidative Stress in Alzheimer's Disease; Can Nutraceuticals and Functional Foods Come to the Rescue?

Alzheimer's disease (AD), the most prevalent form of age-related dementia, is typified by progressive memory loss and spatial awareness with personality changes. The increasing socioeconomic burden associated with AD has made it a focus of extensive research. Ample scientific evidence supports the role of neuroinflammation and oxidative stress in AD pathophysiology, and there is increasing research into the possible role of anti-inflammatory and antioxidative agents as disease modifying therapies. While, the result of numerous preclinical studies has demonstrated the benefits of anti-inflammatory agents, these benefits however have not been replicated in clinical trials, necessitating a further search for more promising anti-inflammatory agents. Current understanding highlights the role of diet in the development of neuroinflammation and oxidative stress, as well as the importance of dietary interventions and lifestyle modifications in mitigating them. The current narrative review examines scientific literature for evidence of the roles (if any) of dietary components, nutraceuticals and functional foods in the prevention or management of AD. It also examines how diet/ dietary components could modulate oxidative stress/inflammatory mediators and pathways that are crucial to the pathogenesis and/or progression of AD.

Identification of molecular signatures associated with sleep disorder and Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized.

METHODS

Differentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls.

RESULTS

DEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder.

CONCLUSION

AD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.